R

The function in R that will calculate a mean is called mean().

mean(my_data$`sales-price`)

## [1] 110382.4

Excel

In Excel, the function is AVERAGE().

R

In R, the function sd() will calculate a standard deviation.

sd(my_data$`sales-price`)

## [1] 71810.03

Excel

In Excel, the function to calculate a standard deviation is STDEV.P().

R

In R, you can calculate a percentile with the function quantile(). The median is the 50th percentile.

quantile(my_data$`sales-price`, probs = 0.5)

##      50% 
## 93083.57

You can also calculate the 25th and 75th percentiles.

quartiles <- quantile(my_data$`sales-price`, probs = c(0.25, 0.75))

quartiles

##       25%       75% 
##  62351.48 137676.30

And the difference between them is the interquartile range.

IQR = quartiles["75%"] - quartiles["25%"]

as.numeric(IQR)

## [1] 75324.81

Excel

In Excel, the function to calculate percentiles is PERCENTILE.EXC().

R

In R, you can use the table() function to get the number of observations in each category.

table(my_data$building)

## 
##        Duplex Single-family    Three-plus 
##          2353          5305          2342

And then you can express these as proportions by dividing those numbers by the number of observations.

table(my_data$building) / nrow(my_data)

## 
##        Duplex Single-family    Three-plus 
##        0.2353        0.5305        0.2342

Excel

If you’ve set up a column for each category, where a value of 1 means the observation is in that category and a value of zero means it is not, the average of one of those columns is the proportion of observations within that category.

You can use an IF() function in Excel to convert categories to zeros and ones.

R

Here is how you would do a one-sample t-test in R.

t.test(my_data$`sales-price`)

## 
##  One Sample t-test
## 
## data:  my_data$`sales-price`
## t = 153.71, df = 9999, p-value < 2.2e-16
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  108974.7 111790.0
## sample estimates:
## mean of x 
##  110382.4

By default, the t.test function returns a 95-percent confidence interval, but you can also specify other confidence levels. Here is how you would get the 99-percent confidence level.

t.test(my_data$`sales-price`,
       conf.level = 0.99)

## 
##  One Sample t-test
## 
## data:  my_data$`sales-price`
## t = 153.71, df = 9999, p-value < 2.2e-16
## alternative hypothesis: true mean is not equal to 0
## 99 percent confidence interval:
##  108532.3 112232.4
## sample estimates:
## mean of x 
##  110382.4

Excel

To calculate the 95-percent confidence interval in Excel, you’d need to know that the confidence margin is the standard deviation times the z-score corresponding to the confidence level you want.

So, first you calculate the standard deviation of the sample.

Then you calculate the z-score for the desired confidence level using the function NORM.INV.S(). The probability you should use for a 95% confidence level is 0.975. Or, if you use a probability of 0.025, you’ll get the opposite (i.e. negative) of the z-score you need.

Now you can calculate the upper and lower bounds of the confidence interval.

R

In R, you use prop.test rather than t.test when you want to find the confidence interval for a proportion.

prop.test(x = sum(my_data$building == "Single-family"),
          n = nrow(my_data))

## 
##  1-sample proportions test with continuity correction
## 
## data:  sum(my_data$building == "Single-family") out of nrow(my_data), null probability 0.5
## X-squared = 37.088, df = 1, p-value = 1.129e-09
## alternative hypothesis: true p is not equal to 0.5
## 95 percent confidence interval:
##  0.5206585 0.5403179
## sample estimates:
##      p 
## 0.5305

Excel

To calculate the 95-percent confidence interval in Excel, you’d need to know that the confidence margin is the standard error times the z-score corresponding to the confidence level you want.

The standard error is given by the equation:

\[ S.E. = \sqrt{\frac{p\times(1-p)}{n}} \]

Where p is the proportion and n is the number of observations.

Then you calculate the z-score and the upper and lower bounds of the confidence interval as you would for a continuous variable.

R

In R (ggplot2), geom_histogram will generate a histogram.

Here, I’ll visualize the distribution of a variable in my dataset called “store size.”

ggplot(my_data) +
  geom_histogram(aes(x = `sales-price`),
                 bins = 40) +
  theme_linedraw()

Excel

You can create a histogram in Excel using the data analysis tool pack. By default, it will put gaps between the bars. It is more common to show histograms without these gaps, so you can remove them.

R

In R, you can use geom_jitter() to generate a scattplot. You can set the x-coordinate as the variable you want to visualize, and set the y-coordinate as a constant, and R will randomly vary the y positions.

You might want to turn off the y-axis labels since they’ll be meaningless.

You might also want to make the points a little bit transparent to avoid overplotting.

ggplot(my_data) +
  geom_jitter(aes(x = `sales-price`, y = 0),
              alpha = 0.1) +
  theme_linedraw() +
  theme(axis.text.y = element_blank(),
        axis.title.y = element_blank(),
        axis.ticks.y =  element_blank(),
        panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank())

Excel

In Excel, to acheive that same effect, you need to create a fake y-variable that varies randomly.

Then you can create a scatterplot.

R

In R, you can use geom_bar() to represent proportions with a stacked bar chart.

ggplot(my_data) +
  geom_bar(aes(fill = building, x = 1),
           position = "fill") +
  scale_y_continuous(name = "Proportion",
                     breaks = c(0.2,
                                0.4,
                                0.6,
                                0.8,
                                1),
                     labels = c("20%",
                                "40%",
                                "60%",
                                "80%",
                                "100%")) +
  theme_linedraw() +
  theme(axis.text.x = element_blank(),
        axis.title.x = element_blank(),
        axis.ticks.x =  element_blank(),
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank())

ggplot(my_data) +
  geom_bar(aes(x = building)) +
  scale_y_continuous(breaks = breaks <- c(2000, 4000, 6000),
                     labels = paste0(100*breaks / nrow(my_data), "%"),
                     name = "Percentage") +
  theme_linedraw() 

You can also calculate the proportions and their confidence intervals and make a little table for them like this.

prop_summary <- my_data |>
  mutate(single_family = building == "Single-family",
         duplex = building == "Duplex",
         three_plus = building == "Three-plus") |>
  select(single_family, 
         duplex, 
         three_plus) |>
  pivot_longer(cols = everything(),
               names_to = "which_building",
               values_to = "is_type") |>
  group_by(which_building) |>
  summarise(Proportion = mean(is_type),
            `C.I. Low` = prop.test(x = sum(is_type==1),
                                   n = n(), 
                                   conf.level = 0.95)$conf.int[1],
            `C.I. High` = prop.test(x = sum(is_type==1),
                                   n = n(), 
                                   conf.level = 0.95)$conf.int[2])

prop_summary

## # A tibble: 3 × 4
##   which_building Proportion `C.I. Low` `C.I. High`
##   <chr>               <dbl>      <dbl>       <dbl>
## 1 duplex              0.235      0.227       0.244
## 2 single_family       0.530      0.521       0.540
## 3 three_plus          0.234      0.226       0.243

And then we can use that to make a bar chart with error bars

ggplot(prop_summary,
       aes(x = which_building,
           y = Proportion)) +
  geom_bar(stat = "identity") +
  geom_errorbar(aes(ymin = `C.I. Low`,
                    ymax = `C.I. High`)) +
  theme_linedraw()

Excel

In Excel, if you set up a little table with the proportions and margins of error like this:

You can create a bar chart like this:

And add error bars like this: